conv net visualization

Author: whyboris

README.md

@@ -3,6 +3,8 @@
 Worked examples following _Deep Learning with Python_ by François Chollet
 It's an amazing [book](https://www.manning.com/books/deep-learning-with-python) - likely the fastest way to get started with _machine learning_!
 
+Source code from the book available on [GitHub](https://github.com/fchollet/deep-learning-with-python-notebooks)
+
 ### notes
 
 This is WIP. Feel free to reach out with questions via _Issues_

visualize-convnet.py

@@ -0,0 +1,78 @@
+print('visualize convnet')
+
+from hack import hack
+hack()
+
+from keras.models import load_model
+model = load_model('cats_and_dogs_small.h5')
+
+# model.summary()
+
+img_path = '../catsdogssmall/test/cats/cat.1700.jpg'
+
+from keras.preprocessing import image
+import numpy as np 
+
+img = image.load_img(img_path, target_size=(150, 150))
+
+img_tensor = image.img_to_array(img)
+img_tensor = np.expand_dims(img_tensor, axis=0)
+img_tensor /= 255.
+
+print(img_tensor.shape)
+
+import matplotlib.pyplot as plt 
+
+# plt.imshow(img_tensor[0])
+# plt.show()
+
+from keras import models
+
+layer_outputs = [layer.output for layer in model.layers[:8]]
+activation_model = models.Model(inputs=model.input, outputs=layer_outputs)
+
+activations = activation_model.predict(img_tensor)
+
+# first_layer_activation = activations[1]
+# print(first_layer_activation.shape)
+
+# plt.matshow(first_layer_activation[0, :, :, 7], cmap='viridis')
+# plt.show()
+
+layer_names = []
+for layer in model.layers[:8]:
+    layer_names.append(layer.name)
+
+images_per_row = 16
+
+for layer_name, layer_activation in zip(layer_names, activations):
+    n_features = layer_activation.shape[-1]
+
+    size = layer_activation.shape[1]
+
+    n_cols = n_features // images_per_row
+    display_grid = np.zeros((size * n_cols, images_per_row * size))
+
+    for col in range (n_cols):
+        for row in range(images_per_row):
+            channel_image = layer_activation[0, :, :, col * images_per_row + row]
+
+            channel_image -= channel_image.mean()
+            channel_image /= (channel_image.std() + 0.00001) # so it's not zero ever!
+            channel_image *= 64
+            channel_image += 128
+            channel_image = np.clip(channel_image, 0, 255).astype('uint8')
+            display_grid[col * size : (col + 1) * size,
+                         row * size : (row + 1) * size] = channel_image
+    
+    scale = 1./ size
+    plt.figure(figsize=(scale * display_grid.shape[1],
+                        scale * display_grid.shape[0]))
+
+    plt.title(layer_name)
+    plt.grid(False)
+    plt.imshow(display_grid, aspect='auto', cmap='viridis')
+
+plt.show()
+
+